From the 1 of 9 linked papers with an AI index.
9 papers
GeCo: Evaluating Geometric Consistency for Video Generation via Motion and Structure
Leslie Gu, Junhwa Hur, Charles Herrmann +4
GeCo is a geometry-based metric that detects deformation and occlusion inconsistencies in generated videos by combining residual motion and depth cues, providing dense consistency…
CityRAG: Stepping Into a City via Spatially-Grounded Video Generation
Gene Chou, Charles Herrmann, Kyle Genova +6
We address the problem of generating a 3D-consistent, navigable environment that is spatially grounded: a simulation of a real location. Existing video generative models can produc…
LoGeR: Long-Context Geometric Reconstruction with Hybrid Memory
Junyi Zhang, Charles Herrmann, Junhwa Hur +5
Feedforward geometric foundation models achieve strong short-window reconstruction, yet scaling them to minutes-long videos is bottlenecked by quadratic attention complexity or lim…
UFO-4D: Unposed Feedforward 4D Reconstruction from Two Images
Junhwa Hur, Charles Herrmann, Songyou Peng +4
Dense 4D reconstruction from unposed images remains a critical challenge, with current methods relying on slow test-time optimization or fragmented, task-specific feedforward model…
Force Prompting: Video Generation Models Can Learn and Generalize Physics-based Control Signals
Nate Gillman, Charles Herrmann, Michael Freeman +4
Recent advances in video generation models have sparked interest in world models capable of simulating realistic environments. While navigation has been well-explored, physically m…
MASIV: Toward Material-Agnostic System Identification from Videos
Yizhou Zhao, Haoyu Chen, Chunjiang Liu +7
System identification from videos aims to recover object geometry and governing physical laws. Existing methods integrate differentiable rendering with simulation but rely on prede…